JWSS - Journal of Water and Soil Science

The steel industry plays an important role in the release of toxic pollutants, including heavy metals, into the environment. The present descriptive-applied study was conducted in 2022 to identify the sources of heavy metal emissions in surface soils in the vicinity of a steel industry using positive matrix factor and chemical mass balance models. Soil samples (50 samples) were systematically collected from four areas within the steel plant, and a control area of 15 km was established. Five main sources, including the earth's crust (factor 1), vehicles (factor 2), steel industry (factor 3), biomass (factor 4), and other sources (factor 5), were identified as the main factors of heavy metals in the positive matrix factor model. Cobalt, nickel, and zinc had the highest mean concentrations with values of 14.5, 1.21, and 0.92mg kg-1, respectively. Cadmium and Lead showed the lowest concentrations with values of 0.02 and 0.10 mg kg-1 in samples inside and outside the Khuzestan Steel Company area, respectively. Comparing the contribution of different sources in the release of heavy metals in the identified factors showed that the steel industry, other sources, earth's crust, vehicles, and biomass accounted for 24, 23, 19, 18, and 16 percent in the positive matrix factor model and 23, 22, 20, 18, and 17 percent in the chemical mass balance model, respectively. The positive matrix factor and chemical mass balance models showed that there was a high level of soil contamination with heavy metals in the vicinity of the Khuzestan Steel Company in Ahvaz city.

Scour is a major challenge in river engineering, as it causes bridge failures during flood events and leads to significant economic losses. This study aims to estimate the normalized scour depth (Dse/Dp) around pile groups by examining relevant hydrodynamic and geometric parameters. A dataset comprising 299 laboratory measurements collected from various sources was assembled and divided into training and testing subsets. As machine learning inputs, several models were employed, including Artificial Neural Networks (ANN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and a meta-ensemble learning model (Stacking). Hyperparameter tuning was performed using the Grid search method to achieve optimal regression performance. Model performance evaluation indicated that the ANN and SVR models achieved coefficients of determination of R² = 0.87 and R² = 0.91, respectively. The XGBoost model outperformed these approaches, yielding R² = 0.94 with an RMSE of approximately 0.28. Ultimately, the stacking ensemble model, by integrating the outputs of the base learners, demonstrated the highest predictive accuracy with R² = 0.96 and an RMSE of 0.11, representing an improvement of approximately 15% compared to ANN and 7% compared to XGBoost. Overall, the findings highlight that ensemble machine learning models—particularly the Stacking approach—provide a robust and efficient framework for predicting scour depth around pile groups and for capturing the complex flow behaviors in hydraulic systems.

Greenhouse gas emissions and achieving acceptable water and energy efficiency are among the most important challenges facing the agricultural sector. The objective of the current research was to investigate the indicators of water physical and economic productivity and energy of wheat in Khorasan Razavi Province. To evaluate these indicators, a questionnaire was used in this research. A total of 200 questionnaires, including 50 for each city, were distributed, and the amount of input consumption and production was collected. The results showed that the physical productivity of water in the plains of Mashhad, Torbat Jam, Taybad, and Bakharz was 0.57, 0.72, 0.7, and 0.46 kg/m3, respectively. Also, the results showed that the highest Energy efficiency and energy productivity were 2.18 and 0.148 kg/MJ, respectively, for the Taybad Plain, and the highest specific energy was 10.92 MJ/kg for the Bakharz Plain. The highest (1539/68 kg/ha) and lowest (964/79 kg/ha) greenhouse gas emissions were obtained in the Mashhad and Taybad plains, respectively. The overall results showed that crop yield in relatively arid areas such as Torbat-e Jam and Taybad is higher than in semi-arid areas with higher altitudes, similar to Mashhad and Bakhrez.